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This study investigates the computational challenges and opportunities presented by the agglutinative structures in Urdu, a language characterized by its complex system of morpheme-based word formation. Agglutinative languages, including Urdu, pose significant difficulties in natural language processing (NLP) due to the intricate ways in which morphemes each carrying distinct grammatical or semantic meanings are combined to form words. Despite its linguistic richness and central role among South Asian languages, Urdu has been relatively underrepresented in global computational research, leading to a lack of robust NLP tools tailored to its unique morphological features. This gap highlights the need for extensive linguistic resources, including annotated corpora and models that can specifically address the complexities of Urdu's agglutinative morphology, which remain largely unexplored. Using the Emille Urdu Corpus, this research systematically analyzes the frequency and distribution of agglutinative structures in Urdu. A Python-based annotation process was employed to tag prefixes and suffixes, facilitating a more granular understanding of Urdu morphology. The study highlights key patterns, such as the prevalent use of prefixes like "نا-" (nā-) and "بد-" (bad-) to form words with negative connotations and the transformation of adjectives and verbs into nouns through suffixes like "-گی " (gī) and "-ی" (ī). Furthermore, the research explores the limitations of traditional rule-based models in handling Urdu’s morphological complexity and advocates for the adoption of machine learning and deep learning techniques. These modern approaches, particularly Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), show promise in accurately modeling Urdu's agglutinative morphology, though they require extensive linguistic data and computational resources. The findings underscore the need for comprehensive linguistic resources and advanced computational models to enhance Urdu NLP. By addressing these challenges, the study aims to contribute to the development of more effective and scalable NLP tools, thereby improving access to Urdu-language content in digital platforms and advancing the broader field of computational linguistics for agglutinative languages.

Utilizing zeolitic imidazolate frameworks (ZIFs) poses a significant challenge that demands a facile synthesis method to produce uniform and nanometer-scale materials with high surface areas while achieving high yields. Herein, we demonstrate a facile and cost-effective strategy to systematically produce ZIF8 nanocrystals. Typically, ZIF8 nanocrystal synthesis involves a wet chemical route. As the reaction time decreased (150, 120, and 90 min), the size of the ZIF8 crystals decreased with uniform morphology, and productivity reached as high as 89%. The composition of the product was confirmed through XRD, FE-SEM, TEM, EDS, and Raman spectroscopy. The ZIF8 synthesized with different reaction time was finally employed for catalyzing the electrochemical hydrogen evaluation reaction (HER). The optimized ZIF8-3 obtained at 90 min of reaction time exhibited a superior catalytic action on the HER in alkaline medium, along with a remarkably long-term stability for 24 h compared with the other ZIF8 nanocrystals obtained at different reaction times. Specifically, the optimized ZIF8-3 sample revealed an HER overpotential of 172 mV and a Tafel slope of 104.15 mV·dec−1. This finding, thus, demonstrates ZIF8 as a promising electrocatalyst for the production of high-value-added green and sustainable hydrogen energy.